/// <summary>
/// Returns a list with floats ranging between 0 and 1
/// </summary>
/// <param name="input"></param>
/// <returns></returns>
public List <float> GetNext()
{
List <float> input = Input;
//update it to hold new input
if (input.Count != weights[0].Length)
{
float[][] newInputLayer = new float[input.Count][];
float[] weightConnections;
for (int neuron = 0; neuron < weights[0].Length; neuron++)
{
weightConnections = weights[0][neuron];
for (int weight = 0; weight < input.Count; weight++)
{
newInputLayer[neuron][weight] =
weight < weightConnections.Length ?
weightConnections[weight] : memoryPrefab[neuron][weight - input.Count];
}
}
weights[0] = newInputLayer;
}
//nX = neurons list length, nX = neurons y length, nZ = next neurons list length
int nX = neurons.Length - 1, nY, nZ;
float res;
List <float> results = input, newInput = new List <float>();
for (int neuronList = 0; neuronList < nX; neuronList++)
{
nY = neurons[neuronList].Length;
nZ = neurons[neuronList + 1].Length;
for (int i = 0; i < nZ; i++)
{
newInput.Add(0);
}
for (int neuron = 0; neuron < nY; neuron++)
{
res = results[neuron];
for (int weightConnection = 0; weightConnection < nZ; weightConnection++)
{
newInput[weightConnection] += res * weights[neuronList][neuron][weightConnection];
}
}
results = Methods.CloneList(newInput);
for (int f = 0; f < results.Count; f++)
{
results[f] = Methods.Activation(results[f]);
}
newInput.Clear();
}
//change it from -1 and 1 to 0 and 1
for (int returnable = 0; returnable < results.Count; returnable++)
{
results[returnable] = (results[returnable] + 1) / 2;
}
return(results);
}
